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TRENDS IN Public Disclosure Authorized MATERNAL MORTALITY 2000 to 2017
Estimates by WHO, UNICEF, UNFPA, World Bank Group and
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Estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division Trends in maternal mortality 2000 to 2017: estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division ISBN 978-92-4-151648-8
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Technical and copyediting: Green Ink (www.greenink.co.uk). Design and layout: Anne-Marie Labouche CONTENTS
Acknowledgments...... vi Executive summary...... ix 1. Introduction...... 1 2. Definitions and measures...... 7 2.1 Definitions for key terms used in this report...... 8 2.2 Measures of maternal mortality used in this report...... 9 3. Methods ...... 13 3.1 Data inputs for the estimation process...... 14 3.1.1 Data sources...... 14 3.1.2 Uncertainty associated with observations and adjustments...... 16 3.2. Other data inputs to the model...... 17 3.2.1 Data on all deaths to women aged 15–49 years and HIV-related mortality...... 17 3.2.2 Live births data...... 18 3.2.3 Predictor variables in the maternal mortality model...... 18 3.3. Statistical methods...... 18 3.3.1 Bayesian CRVS adjustment model to account for errors in reporting of maternal death in the CRVS system (the CRVS model)...... 19 3.3.2 Bayesian maternal mortality estimation model (the BMat model)...... 24 3.3.3 Maternal mortality indicators estimated by the model...... 28 4. Maternal Mortality estimates and trends: 2000 to 2017 ...... 31 4.1 Maternal mortality estimates for 2017...... 32 4.1.1 Regional-level estimates...... 33 4.1.2 Country-level estimates...... 34 4.2 Trends in maternal mortality: 2000 to 2017...... 39 4.2.1 Regional-level trends...... 39 4.2.2 Country-level trends...... 40 4.3 Comparison with previous maternal mortality estimates...... 42 5. Assessing progress and setting a trajectory towards ending preventable maternal mortality and achieving SDG target 3.1...... 43 5.1 Transition from MDG to SDG reporting...... 44 5.2. Strategies for improving maternal health: 2016 to 2030...... 46 5.2.1 Specialized population groups: humanitarian and crisis settings, vulnerable populations and late maternal deaths...... 46 5.2.2 Challenges remain: need for improved civil registration and vital statistics (CRVS) systems and other data sources...... 47 6. Conclusions...... 51 Annexes...... 55
Additional relevant materials including links to the full database, country profiles and all model specification codes, as well as language editions of this report (when available) can be found at: www.who.int/reproductivehealth/publications/maternal-mortality-2017/en/
iii LIST OF TABLES
Table 3.1. Maternal mortality data records by source type used in generating maternal mortality ratio estimates (MMR, maternal deaths per 100 000 live births) for 2017
Table 4.1. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, lifetime risk and proportion of deaths among women of reproductive age that are due to maternal causes (PM), by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2017
Table 4.2. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths and HIV-related indirect maternal deaths, by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2017
Table 4.3. Comparison of maternal mortality ratio (MMR, maternal deaths per 100 000 live births) and number of maternal deaths, by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2000 and 2017
iv LIST OF ANNEXES
Annex 1. Summary description of the country consultations 2019
Annex 2. Measuring maternal mortality
Annex 3. Calculation of maternal mortality during crisis years
Annex 4. Methods used to derive a complete series of annual estimates for each predictor variable
Annex 5. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, lifetime risk, percentage of HIV-related indirect maternal deaths and proportion of deaths among women of reproductive age that are due to maternal causes (PM), by country and territory, 2017
Annex 6. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by World Health Organization (WHO) region, 2017
Annex 7. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by WHO region, 2000–2017
Annex 8. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by United Nations Children’s Fund (UNICEF) region, 2017
Annex 9. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by UNICEF region, 2000–2017
Annex 10. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by United Nations Population Fund (UNFPA) region, 2017
Annex 11. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by UNFPA region, 2000–2017
Annex 12. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by World Bank Group region and income group, 2017
Annex 13. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by World Bank Group region and income group, 2000–2017
Annex 14. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by United Nations Population Division (UNPD) region, 2017
Annex 15. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by UNPD region, 2000–2017
Annex 16. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2000–2017
Annex 17. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by country and territory, 2000–2017
v
ACKNOWLEDGMENTS
The United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG), together with its independent external Technical Advisory Group (TAG), collaborated in developing these maternal mortality estimates.
From each of the constituent agencies that form the UN MMEIG, the following individuals worked on the compilation of this report:1 • World Health Organization (WHO): Doris Chou, Ann-Beth Moller and Lale Say • United Nations Children’s Fund (UNICEF): Liliana Carvajal-Aguirre and Jennifer Requejo • United Nations Population Fund (UNFPA): Tapiwa Jhamba • United Nations Population Division (UNPD, a division of the United Nations Department of Economic and Social Affairs [UN DESA]): Kirill Andreev, Lina Bassarsky, Victor Gaigbe-Togbe and Patrick Gerland • The World Bank Group: Charles Kouame, Samuel Mills and Emi Suzuki.
The members of the TAG provided independent technical advice: • Saifuddin Ahmed, of Johns Hopkins Bloomberg School of Public Health, United States of America (USA) • Peter Byass, of the Umeå Centre for Global Health Research, Umeå University, Sweden • Thomas W. Pullum, of the Demographic and Health Surveys (DHS) Program, ICF, USA.
In addition, independent expert consultants for this project were:
• Tim Colbourn, of University College London, United Kingdom of Great Britain and Northern Ireland • Jeff Eaton, of Imperial College London, United Kingdom • Alison Gemmill and Stéphane Helleringer, of Johns Hopkins University, USA • Marie Klingberg Alvin, of Dalarna University/Högskolan Dalarna, Sweden • Laina Mercer, of PATH, USA • Helena Nordenstedt, of the Karolinska Institutet, Sweden • Jon Wakefield, of the University of Washington, USA.
The TAG is grateful for the review and support of a working group on maternal mortality in censuses. The work was supported by funding from the United States Agency for International Development (USAID) through MEASURE Evaluation (cooperative agreement AID-OAA-L-14-00004). The members of the working group were:
• Liliana Carvajal-Aguirre, of UNICEF • Doris Chou, of WHO • Patrick Gerland, of UNPD • Peter Johnson (retired), Nobuko Mizoguchi and Loraine West (retired) of the United States Census Bureau, USA • Qingfeng Li, of Johns Hopkins Bloomberg School of Public Health, USA • Kavita Singh Ongechi, of the University of North Carolina at Chapel Hill, USA.
We are also grateful to the WHO Department of Governing Bodies and External Relations. Country offices for WHO, UNICEF, UNFPA and the World Bank Group are all gratefully acknowledged for facilitating the country consultations.
1 All lists of names are given in alphabetical order by last name.
vi Thanks are also due to the following WHO regional office staff:
• Regional Office for Africa: Elongo Lokombe, Triphonie Nkurunziza, Léopold Ouedraogo and Prosper Tumusiime • Regional Office for the Americas (Pan American Health Organization [PAHO]): Adrienne Lavita Cox, Bremen de Mucio, Patricia Lorena Ruiz Luna, Antonio Sanhueza and Suzanne Serruya • Regional Office for South-East Asia: C. Anoma Jayathilaka, Mark Landry and Neena Raina • Regional Office for Europe: Nino Berdzuli, Kristina Mauer-Stender, David Novillo and Claudia Stein • Regional Office for the Eastern Mediterranean: Karima Gholbzouri, Ramez Khairi Mahaini and Arash Rashidian • Regional Office for the Western Pacific: Jun Gao, Priya Mannava and Howard Sobel.
In addition, WHO provided translation services for documents disseminated during the country consultations. Thanks to Patricia Lorena Ruiz Luna, Antonio Sanhueza and Rosina Romero, of PAHO, for all their translation support for communications during the country consultations. Thank you to all government technical focal persons for maternal mortality and the Sustainable Development Goal (SDG) focal points who reviewed the preliminary maternal mortality estimates and provided valuable feedback and input.
Financial support was provided by WHO, through the Department of Reproductive Health and Research and HRP (the UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction), USAID and the University of Massachusetts, Amherst, USA.
Thanks also go to Alison Gemmill and Kerry Wong for helping with the country profiles; to Jenny Cresswell, Carolin Ekman and Doris Hanappi for helping with data review; to Florence Rusciano for assistance with creation of maps; and to Catherine Hamill, Svetlin Kolev and Christine Meynent for assistance with related webpages. This report was prepared by Doris Chou, Ann-Beth Moller and Lale Say of the WHO Department of Reproductive Health and Research; Leontine Alkema and Emily Peterson of the University of Massachusetts, USA; and Jane Patten of Green Ink, United Kingdom.
For any further information relating to this report, you may contact Doris Chou (email: [email protected]) and Lale Say (email: [email protected]) of the WHO Department of Reproductive Health and Research.
vii
ACRONYMS AND ABBREVIATIONS
ARR annual rate of reduction ASFR age-specific fertility rates BMat Bayesian maternal mortality estimation model CEMD confidential enquiry into maternal deaths CRVS civil registration and vital statistics DHS Demographic and Health Survey EPMM ending preventable maternal mortality F+/F– false positive/false negative GDP gross domestic product per capita based on PPP conversion GFR general fertility rate ICD International statistical classification of diseases and related health problems2 ICD-MM ICD-maternal mortality (refers to WHO publication: Application of ICD-10 to deaths during pregnancy, childbirth and the puerperium: ICD-MM) MDG Millennium Development Goal MDSR maternal death surveillance and response MICS Multiple Indicator Cluster Survey MMR maternal mortality ratio MMRate maternal mortality rate PM proportion maternal (i.e. proportion of deaths among women of reproductive age that are due to maternal causes) PPP purchasing power parity SBA skilled birth attendant SDG Sustainable Development Goal T+/T– true positive/true negative TAG technical advisory group UI uncertainty interval UNAIDS Joint United Nations Programme on HIV/AIDS UNFPA United Nations Population Fund UNICEF United Nations Children’s Fund UN MMEIG United Nations Maternal Mortality Estimation Inter-Agency Group UNPD United Nations Population Division (in the Department of Economic and Social Affairs) WHO World Health Organization
2 ICD-9, ICD-10 and ICD-11 are all referred to in this document; the numbers indicate the revision (edition) number.
viii TRENDS IN MATERNAL MORTALITY EXECUTIVE SUMMARY
The Sustainable Development Goals (SDGs) were launched on 25 September 2015 and came into force on 1 January 2016 for the 15-year period until 31 December 2030. Among the 17 SDGs, the direct health-related targets come under SDG 3: Ensure healthy lives and promote well-being for all at all ages. With the adoption of the SDGs, the United Nations Member States extended the global commitments they had made in 2000 to the Millennium Development Goals (MDGs), which covered the period until 2015.
In anticipation of the launch of the SDGs, the World Health Organization (WHO) and partners released a consensus statement and full strategy paper on ending preventable maternal mortality (EPMM). The EPMM target for reducing the global maternal mortality ratio (MMR) by 2030 was adopted as SDG target 3.1: reduce global MMR to less than 70 per 100 000 live births by 2030.
Having targets for mortality reduction is important, but accurate measurement of maternal mortality remains challenging and many deaths still go uncounted. Many countries still lack well functioning civil registration and vital statistics (CRVS) systems, and where such systems do exist, reporting errors – whether incompleteness (unregistered deaths, also known as “missing”) or misclassification of cause of death – continue to pose a major challenge to data accuracy.
ix Methods and interpretation due to modifications in methodology and data availability, differences between these and The United Nations Maternal Mortality previous estimates should not be interpreted Estimation Inter-Agency Group (UN MMEIG) – as representing time trends. In addition, when comprising WHO, the United Nations Children’s interpreting changes in MMRs over time, one Fund (UNICEF), the United Nations Population should take into consideration that it is easier Fund (UNFPA), the World Bank Group and the to reduce the MMR when the level is high United Nations Population Division (UNPD) of than when the MMR level is already low. The the Department of Economic and Social Affairs full database, country profiles and all model – has collaborated with external technical specification codes used are available online.5 experts on a new round of estimates for 2000–2017. To provide increasingly accurate Global estimates for 2017 and MMR estimates, the previous estimation trends for 2000–2017 methods have been refined to optimize use of country-level data. Consultations with The global estimates for the year 2017 indicate countries were carried out during May and that there were 295 000 (UI 279 000 to June 2019. This process generated additional 340 000)6 maternal deaths; 35% lower than in data for inclusion in the maternal mortality 2000 when there were an estimated 451 000 estimation model, demonstrating widespread (UI 431 000 to 485 000) maternal deaths. The expansion of in-country efforts to monitor global MMR in 2017 is estimated at 211 (UI 199 maternal mortality. to 243) maternal deaths per 100 000 live births, representing a 38% reduction since 2000, This report presents internationally comparable when it was estimated at 342. The average global, regional and country-level estimates annual rate of reduction (ARR) in global MMR and trends for maternal mortality between during the 2000–2017 period was 2.9%; this 2000 and 2017.3 Countries and territories means that, on average, the global MMR included in the analyses are WHO Member declined by 2.9% every year between 2000 States with populations over 100 000, plus two and 2017. The global lifetime risk of maternal territories (Puerto Rico, and the West Bank mortality for a 15-year-old girl in 2017 was and Gaza Strip)4. The results described in estimated at 1 in 190; nearly half of the level of this report are the first available estimates for risk in 2000: 1 in 100. The overall proportion of maternal mortality in the SDG reporting period; deaths to women of reproductive age (15–49 but since two years (2016 and 2017) is not years) that are due to maternal causes (PM) sufficient to show trends, estimates have been was estimated at 9.2% (UI 8.7% to 10.6%) in developed and presented covering the period 2017 – down by 26.3% since 2000. This means 2000 to 2017. The new estimates presented in that compared with other causes of death this report supersede all previously published to women of reproductive age, the fraction estimates for years that fall within the same attributed to maternal causes is decreasing. In time period. Care should be taken to use only addition, the effect of HIV on maternal mortality these estimates for the interpretation of trends in 2017 appears to be less pronounced than in in maternal mortality from 2000 to 2017; earlier years; HIV-related indirect maternal
3 Estimates have been computed to ensure comparability 5 Available at: www.who.int/reproductivehealth/ across countries, thus they are not necessarily the same as publications/maternal-mortality-2017/en/ official statistics of the countries, which may use alternative 6 All uncertainty intervals (UIs) reported are 80% UI. The rigorous methods. data can be interpreted as meaning that there is an 80% 4 Puerto Rico is an Associate Member, and the West Bank chance that the true value lies within the UI, a 10% chance and Gaza Strip is a member in the regional committee for the that the true value lies below the lower limit and a 10% WHO Eastern Mediterranean Region. chance that the true value lies above the upper limit.
x TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 deaths now account for approximately 1% of Three countries are estimated to have had all maternal deaths compared with 2.5% in extremely high MMR in 2017 (defined as over 2005, at the peak of the epidemic. 1000 maternal deaths per 100 000 live births): South Sudan (1150; UI 789 to 1710), Chad Regional and country-level (1140; UI 847 to 1590) and Sierra Leone (1120; estimates for 2017 UI 808 to 1620). Sixteen other countries, all also in sub-Saharan Africa except for one MMR in the world’s least developed countries (Afghanistan), had very high MMR in 2017 (LDCs) is high,7 estimated at 415 maternal (i.e. estimates ranging between 500 and 999). deaths per 100 000 live births (UI 396 to 477), Only three countries in sub-Saharan Africa had which is more than 40 times higher than that low MMR: Mauritius (61; UI 46 to 85), Cabo for MMR the in Europe (10; UI 9 to 11), and Verde (58; UI 45 to 75) and Seychelles (53; almost 60 times higher than in Australia and UI 26 to 109). Only one country outside the New Zealand (7; UI 6 to 8). In the world’s LDCs, sub-Saharan African region had high MMR: where an estimated 130 000 maternal deaths Haiti (480; UI 346 to 718). Ninety countries occurred in 2017, the estimated lifetime risk were estimated to have MMR of 50 or less in of maternal death was 1 in 56. Sub-Saharan 2017. Africa is the only region with very high MMR for 2017, estimated at 542 (UI 498 to 649), Nigeria and India had the highest estimated while the lifetime risk of maternal death was 1 numbers of maternal deaths, accounting for in 37, compared with just 1 in 7800 in Australia approximately one third (35%) of estimated and New Zealand. Moderate MMR (100–299) global maternal deaths in 2017, with was estimated in Northern Africa, Oceania approximately 67 000 and 35 000 maternal (excluding Australia and New Zealand), deaths (23% and 12% of global maternal Southern Asia, South-Eastern Asia and in small deaths), respectively. Three other countries island developing states. Four subregions also had 10 000 maternal deaths or more: the (Australia and New Zealand, Central Asia, Democratic Republic of the Congo (16 000), Eastern Asia, Western Asia) and two regions Ethiopia (14 000) and the United Republic (Latin America and the Caribbean, and Europe of Tanzania (11 000). Sixty-one countries and Northern America) have low MMR (< 100 were estimated to have had just 10 or fewer maternal deaths per 100 000 live births). maternal deaths in 2017.
Sub-Saharan Africa and Southern Asia In 2017, according to the Fragile States Index, accounted for approximately 86% (254 000) of 15 countries were considered to be “very the estimated global maternal deaths in 2017 high alert” or “high alert”8 (from highest to with sub-Saharan Africa alone accounting for lowest: South Sudan, Somalia, Central African roughly 66% (196 000), while Southern Asia Republic, Yemen, Syrian Arab Republic, accounted for nearly 20% (58 000). South- Sudan, the Democratic Republic of the Eastern Asia, in addition, accounted for over Congo, Chad, Afghanistan, Iraq, Haiti, Guinea, 5% of global maternal deaths (16 000).
8 The Fragile States Index is an assessment of 178 countries based on 12 cohesion, economic, social and political indicators, resulting in a score that indicates their susceptibility to instability. Further information about indicators and methodology is available at: https:// fragilestatesindex.org/. At the top of the range (most fragile), 7 For the purpose of categorization, MMR is considered to the scores are categorized as follows: > 110 = very high be low if it is less than 100, moderate if it is 100–299, high if it alert; 100–110 = high alert. These two categories include the is 300–499, very high if it is 500–999 and extremely high if it 15 most fragile countries mentioned here. There are 10 other is equal to or higher than 1000 maternal deaths per 100 000 categories ranging from “very sustainable” to “alert”, which live births. include the remaining 163 countries.
xi Executive summary Nigeria, Zimbabwe and Ethiopia), and these 15 quality health services must be considered in countries had MMRs in 2017 ranging from 31 crisis and other unstable situations. (Syrian Arab Republic) to 1150 (South Sudan). Countries that achieved the highest ARRs Regional and country-level trends, between 2000 and 2017 (an average ARR of 2000–2017 7% or above), starting with the highest, were Belarus, Kazakhstan, Timor-Leste, Rwanda, Between 2000 and 2017, the subregion of Turkmenistan, Mongolia, Angola and Estonia. Southern Asia achieved the greatest overall In considering the uncertainty intervals around percentage reduction in MMR: 59% (from 384 their average ARRs, we can only be very sure to 157). This equates to an average ARR of about this high level of acceleration in Belarus, 5.3%. Four other subregions roughly halved Kazakhstan, Timor-Leste and Rwanda. In 13 their MMRs during this period: Central Asia countries, MMR increased in the same period. (52%), Eastern Asia (50%), Europe (53%) In considering the uncertainty around the rate and Northern Africa (54%). MMR in LDCs and direction of change, we believe there have also declined by 46%. Despite its very high been true MMR increases in the United States MMR in 2017, sub-Saharan Africa as a region of America and the Dominican Republic. These also achieved a substantial reduction in MMR findings must be considered in context – as of roughly 38% since 2000. Notably, one many factors may drive positive and negative subregion with very low MMR (12) in 2000 – trends in maternal mortality. Northern America – had an increase in MMR of almost 52% during this period, rising to 18 Conclusions in 2017. This is likely related to already low levels of MMR, as well as improvements in The SDGs include a direct emphasis on data collection, changes in life expectancy reducing maternal mortality while also and/or changes in disparities between highlighting the importance of moving subpopulations. beyond survival. Despite the ambition to end preventable maternal deaths by 2030, The greatest declines in proportion of deaths the world will fall short of this target by more among women of reproductive age that are than 1 million lives with the current pace of due to maternal causes (PM) occurred in two progress. There is a continued urgent need for regions: Central and Southern Asia (56.4%), maternal health and survival to remain high on and Northern Africa and Western Asia (42.6%). the global health and development agenda; Almost no change was seen in PM in Europe the state of maternal health interacts with and and Northern America. reflects efforts to improve the accessibility and quality of care. The 2018 Declaration The 10 countries with the highest MMRs in of Astana repositioned primary health care 2017 (in order from highest to lowest: South as the most (cost) effective and inclusive Sudan, Chad, Sierra Leone, Nigeria, Central means of delivering health services to achieve African Republic, Somalia, Mauritania, Guinea- the SDGs. Primary health care is thereby Bissau, Liberia, Afghanistan) all have ARRs considered the cornerstone for achieving between 2000 and 2017 of less than 5%. universal health coverage (UHC), which only When comparing the ARRs between the year exists when all people receive the quality health ranges of 2000–2010 and 2010–2017, these services they need without suffering financial 10 countries have also had stagnant or slowing hardship. Health services that are unavailable/ levels of ARR and therefore remain at greatest inaccessible or of poor quality, however, risk. The impact of interruptions or loss of will not support the achievement of UHC, as
xii TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 envisioned. Efforts to increase the provision of skilled and competent care to more women, before, during and after childbirth, must also be seen in the context of external forces including but not limited to climate change, migration and humanitarian crises – not only because of the environmental risks presented, but also because of their contribution to health complications.
In addition, governments are called upon to establish well functioning CRVS systems with accurate attribution of cause of death. Improvements in measurement must be driven by action at the country level, with governments creating systems to capture data specific to their information needs; systems that must also meet the standards required for international comparability. Globally, standardized methods for preventing errors in CRVS reporting (i.e. incompleteness and misclassification) should be established to enhance international comparability.
In consideration of the above, it must be noted that this report on the levels and trends of maternal mortality provides just one critical facet of information, which synthesizes and draws from the available data, to assess one aspect of global progress towards achieving global goals for improved health and sustainable development. In the context of efforts to achieve UHC, improving maternal health is critical to fulfilling the aspiration to reach SDG 3. One can only hope that the global community will not be indifferent to the shortfalls that are expected if we cannot improve the current rate of reduction in maternal mortality. Ultimately, we need to expand horizons beyond a sole focus on mortality, to look at the broader aspects – country and regional situations and trends including health systems, UHC, quality of care, morbidity levels and socioeconomic determinants of women’s empowerment and education – and ensure that appropriate action is taken to support family planning, healthy pregnancy and safe childbirth.
xiii Executive summary 01
xiv © WHO /Jim Holmes TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 TRENDS 01IN MATERNAL MORTALITY INTRODUCTION
The Sustainable Development Goals (SDGs) were launched on 25 September 2015 with the adoption of the General Assembly resolution Transforming our world: the 2030 Agenda for Sustainable Development (1), and they came into force on 1 January 2016 for the 15-year period until 31 December 2030. Among the 17 SDGs, the direct health-related targets come under SDG 3: Ensure healthy lives and promote well-being for all at all ages (2). With the adoption of the SDGs, the United Nations Member States extended the global commitments they had made in 2000 to the Millennium Development Goals (MDGs), which were established after the Millennium Declaration in September 2000, and covered the period until 2015 (3). Among the eight MDGs, MDG 5 was “Improve maternal health”, and MDG target 5.A was to reduce the 1990 maternal mortality ratio (MMR) by three quarters by 2015 (4). The previous report, published in November 2015, provided estimates and trends for maternal mortality for the period 1990 to 2015 (5); the estimates reported in this new edition supersede those and all earlier estimates.
In 2014, in anticipation of the launch of the SDGs, the World Health Organization (WHO) released a consensus statement on Targets and strategies for ending preventable maternal mortality (EPMM) (6), followed by a full strategy paper in 2015 (7), endorsed
1 by the United Nations Children’s Fund Health (2016–2030), which is aligned with and (UNICEF), the United Nations Population Fund builds upon the SDG 3 targets and time frame, (UNFPA), the World Bank Group, the United and its five key indicators for the “survive” States Agency for International Development objective are MMR (SDG indicator 3.1.1), (USAID), and a number of international under-five mortality rate (SDG indicator 3.2.1), professional organizations and maternal health neonatal mortality rate (SDG indicator 3.2.2), programmes. The EPMM target for MMR stillbirth rate and adolescent mortality rate (the for 2030 was adopted as the SDG updated last two are not SDG indicators) (11). MMR target: reduce global MMR to less than 70 by 2030 (SDG target 3.1) (2,7,8). Meeting Having targets for mortality reduction is this target will require average reductions important, but it must be acknowledged that of about three times the annual rate of accurate measurement of maternal mortality reduction achieved during the MDG era (5) remains challenging and many deaths still go – an enormous challenge. A supplementary uncounted. Planning and accountability for national target was also set in the EPMM improving maternal health, and assessment strategy paper: By 2030, no country should of SDG target 3.1, require accurate and have an MMR greater than 140, a number internationally comparable measures of twice the global target (7). Collective action by maternal mortality. Many countries have made all countries will be needed to reduce national notable progress in collecting data through civil MMR levels in order to bring the global MMR registration and vital statistics (CRVS) systems, down to less than 70 by 2030. Guided by this surveys, censuses and specialized studies EPMM and SDG target, countries have been over the past decade. This laudable increase in setting their own national targets for 2030, efforts to document maternal deaths provides depending on whether their baseline level of valuable new data, but the diversity of methods MMR in 2010 was greater or less than 420; used to assess maternal mortality in the if greater than 420, their target is to reach absence of well functioning CRVS systems MMR of 140 or less by 2030; if less than 420, continues to prevent direct comparisons their target is to reduce MMR by at least two among the data generated. Further country- thirds by 2030 (7). Countries are also called driven efforts are still needed to establish and upon to achieve equity in MMR for vulnerable strengthen CRVS systems so that all births, populations within each country (7). deaths and causes of death are accurately recorded. The updated Global Strategy calls A major initiative established to galvanize for expansion of CRVS systems to increase efforts in the years counting down to the access to services and entitlements, and in conclusion of the MDGs was the United February 2018, UNICEF and WHO committed Nations Secretary-General’s Global Strategy to working with governments and partners to for Women’s and Children’s Health (“the Global strengthen CRVS systems (12). As of March Strategy”), launched in 2010 (9). At the end of 2018, the World Bank Group reported that the MDG era, the Global Strategy was updated over 110 low- and middle-income countries to include adolescents; the Global Strategy had deficient CRVS systems (13). One of the for Women’s, Children’s and Adolescents’ cross-cutting actions called for in the 2015 Health (2016–2030) has as its objectives EPMM strategy paper was to “Improve metrics, “survive, thrive and transform” and is aligned measurement systems and data quality” to with the timeline and priorities of the SDGs ensure that all maternal and newborn deaths (10). In 2016, WHO published the Indicator and are counted: “Counting every maternal and monitoring framework for the Global Strategy perinatal death through the establishment for Women’s, Children’s and Adolescents’ of effective national surveillance and civil
2 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 registration systems in every country … is a that nominated technical focal persons for priority” (7). As tools for this, the strategy paper maternal mortality or that had existing SDG pointed to standard definitions for causes of focal points were provided with estimates death available in the current International for their country and a detailed description statistical classification of diseases and related of the UN MMEIG processes and methods health problems (ICD) manual along with for estimating levels and trends of maternal guidance in The WHO application of ICD-10 mortality. These consultations gave countries to deaths during pregnancy, childbirth and the opportunity to review the draft country puerperium: ICD-MM (14), as well as use of estimates, data sources and methods; maternal death surveillance and response to provide the UN MMEIG with additional (MDSR) systems, perinatal death surveillance, primary data sources that may not have been confidential enquiries into maternal deaths previously reported or used in the analyses; to (CEMD), and other sources of data. However, build shared understanding of the strengths many countries still lack functional CRVS and weaknesses of the available data and systems, and where such systems do exist, the estimation process; and to establish a reporting errors – whether incompleteness broad sense of ownership of the results. (i.e. unregistered deaths, which are also known These country consultations generated as “missing”) or misclassification of cause of additional data for inclusion in the estimation death – continue to pose a major challenge to model, demonstrating widespread expansion data accuracy (15). of in-country efforts to monitor maternal mortality. Annex 1 presents a summary The United Nations Maternal Mortality of the process and results of the country Estimation Inter-Agency Group (UN MMEIG) consultations. – comprising WHO, UNICEF, UNFPA, the World Bank Group and the United Nations This report presents global, regional and Population Division (UNPD) of the Department country-level estimates and trends for of Economic and Social Affairs – has maternal mortality between 2000 and 2017. collaborated with external technical experts Chapter 2 provides the definitions of key terms on a new round of country-level estimates of and describes the key measures relevant to maternal mortality between 2000 and 2017. An maternal mortality. Chapter 3 describes in independent technical advisory group (TAG), detail the methodology employed to develop composed of demographers, epidemiologists the estimates. Chapter 4 presents the and statisticians, provides technical advice. estimates and trends at the global, regional The estimates for 2000–2017 presented in and country levels. Chapter 5 assesses this report are the ninth in a series of analyses performance so far towards SDG target 3.1, by WHO, UNICEF and other United Nations discusses the implications of the estimates partner agencies to examine global, regional for future efforts towards achieving the target, and country progress in reducing maternal and underlines the importance of improved mortality (5,16–22). To provide increasingly data quality for estimating maternal mortality. accurate estimates of MMR, the previous Chapter 6 presents conclusions. The first four estimation methods have been refined to annexes to this report describe the country optimize use of country-level data. consultation process, present an overview of the common approaches for measuring Consultations with countries were carried maternal mortality, describe the methods used out during May and June 2019, following the to derive a complete series of annual estimates development of preliminary MMR estimates for each predictor variable, and to calculate for the years 2000–2017. WHO Member States maternal mortality during crisis years. Finally,
3 Introduction 8. Boldosser-Boesch A, Brun M, Carvajal L, Annexes 5–17 present the MMR estimates and Chou D, de Bernis L, Fogg K, et al. Setting trends for the different regional groupings for maternal mortality targets for the SDGs. Lancet. 2017;389(10070):696-697. doi:10.1016/S0140- SDG reporting and for WHO, UNICEF, UNFPA, 6736(17)30337-9. the World Bank Group and UNPD, as well as 9. Ki-moon B. Global strategy for women’s the country-level estimates and trends. and children’s health. New York (NY): United Nations; 2010 (http://www.who.int/pmnch/ References knowledge/publications/fulldocument_ globalstrategy/en/, accessed 3 December 2015). 1. Transforming our world: the 2030 Agenda for Sustainable Development 2015. Resolution 10. Global strategy for women’s, children’s and adopted by the General Assembly on 25 adolescents’ health (2016–2030). New York September 2015. United Nations General (NY): Every Woman Every Child; 2015 (http:// Assembly, Seventieth session. New York (NY): globalstrategy.everywomaneverychild.org/, United Nations; 2015 (A/RES/70/1; http://www. accessed 10 June 2019). un.org/ga/search/view_doc.asp?symbol=A/ RES/70/1, accessed 28 May 2019). 11. Indicator and monitoring framework for the Global Strategy for Women’s, Children’s and 2. Sustainable Development Goal 3. In: Adolescents’ Health (2016–2030). Geneva: Sustainable Development Goals Knowledge World Health Organization; 2016 (http://www. Platform [website]. New York (NY): United who.int/life-course/publications/gs-Indicator- Nations; 2019 (https://sustainabledevelopment. and-monitoring-framework.pdf, accessed 25 un.org/SDG3, accessed 10 June 2019). July 2019).
3. Conferences, meetings and events: Millennium 12. The future for women and children: UNICEF Summit (6–8 September 2000). In: United and WHO joint statement on strengthening Nations [website]. New York (NY): United civil registration and vital statistics (CRVS). Nations; undated (https://www.un.org/en/ New York (NY) and Geneva: United Nations events/pastevents/millennium_summit.shtml, Children’s Fund and World Health Organization; accessed 5 June 2019). 2018 (https://www.who.int/healthinfo/ civil_registration/WHO_UNICEF_Statement_ 4. Goal 5: Improve maternal health. In: United CRVS_2018.pdf, accessed 29 August 2019). Nations [website]. undated (https://www. un.org/millenniumgoals/maternal.shtml, 13. Global civil registration and vital statistics: accessed 5 June 2019). about CRVS. In: World Bank: Brief [website]. The World Bank Group; 2018 (https://www. 5. World Health Organization (WHO), United worldbank.org/en/topic/health/brief/global- Nations Children’s Fund (UNICEF), United civil-registration-and-vital-statistics, accessed Nations Population Fund (UNFPA), World Bank 29 August 2019). Group, United Nations Population Division. Trends in maternal mortality: 1990 to 2015: 14. The WHO application of ICD-10 to deaths estimates by WHO, UNICEF, UNFPA, World during pregnancy, childbirth and puerperium: Bank Group and the United Nations Population ICD-MM. Geneva: World Health Organization; Division. Geneva: World Health Organization; 2012 (https://www.who.int/reproductivehealth/ 2015 (https://www.who.int/reproductivehealth/ publications/monitoring/9789241548458/en/, publications/monitoring/maternal- accessed 5 June 2019). mortality-2015/en/, accessed 4 September 2019). 15. World Bank Group, World Health Organization. Global civil registration and vital statistics: 6. Targets and strategies for ending preventable scaling up investment plan 2015–2024. Geneva: maternal mortality: consensus statement. World Health Organization; 2014 (https://www. Geneva: World Health Organization; 2014 who.int/healthinfo/civil_registration/WB-WHO_ (https://www.who.int/reproductivehealth/ ScalingUp_InvestmentPlan_2015_2024.pdf, publications/maternal_perinatal_health/ accessed 5 June 2019). consensus-statement/en/, accessed 5 June 2019). 16. World Health Organization (WHO) Maternal Health and Safe Motherhood Programme, 7. Strategies towards ending preventable United Nations Children’s Fund (UNICEF). maternal mortality (EPMM). Geneva: World Revised 1990 estimates of maternal mortality: Health Organization; 2015 (http://www. a new approach by WHO and UNICEF. everywomaneverychild.org/images/EPMM_ Geneva: WHO; 1996 (http://apps.who.int/ final_report_2015.pdf, accessed 5 November iris/bitstream/10665/63597/1/WHO_FRH_ 2015). MSM_96.11.pdf, accessed 28 May 2019).
4 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 17. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA). Maternal mortality in 1995: estimates developed by WHO, UNICEF and UNFPA. Geneva: World Health Organization; 2001 (https://apps.who.int/iris/ handle/10665/66837, accessed 28 May 2019).
18. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA). Maternal mortality in 2000: estimates developed by WHO, UNICEF and UNFPA. Geneva: World Health Organization; 2004 (http://apps.who. int/iris/bitstream/10665/68382/1/a81531.pdf, accessed 5 November 2015).
19. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank. Maternal mortality in 2005: estimates developed by WHO, UNICEF, UNFPA and the World Bank. Geneva: WHO; 2007 (https://www. who.int/whosis/mme_2005.pdf, accessed 28 May 2019).
20. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank. Trends in maternal mortality: 1990 to 2008: estimates developed by WHO, UNICEF, UNFPA and the World Bank. Geneva: WHO; 2010 (https://apps.who.int/iris/bitstream/handle/106 65/44423/9789241500265_eng.pdf, accessed 28 May 2019).
21. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank. Trends in maternal mortality: 1990 to 2010: WHO, UNICEF, UNFPA and the World Bank estimates. Geneva: WHO; 2012 (http://apps.who.int/iris/bitst ream/10665/44874/1/9789241503631_eng.pdf, accessed 28 May 2019).
22. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank, United Nations Population Division. Trends in maternal mortality: 1990 to 2013: estimates by WHO, UNICEF, UNFPA, the World Bank and the United Nations Population Division. Geneva: WHO; 2014 (http://apps.who.int/iris/bitst ream/10665/112682/2/9789241507226_eng. pdf, accessed 28 May 2019).
5 Introduction 02
6 / Abbie Trayler-Smith © H6 Partners CONTENT
8 Definitions for key terms used in this report 9 Measures of maternal mortality used in this report TRENDS 02IN MATERNAL MORTALITY DEFINITIONS AND MEASURES
7 2.1 Definitions for key terms used “resulting from previous existing disease or in this report disease that developed during pregnancy and not due to direct obstetric causes but In the International statistical classification were aggravated by the physiologic effects of diseases and related health problems of pregnancy” (1). For example, deaths due (ICD)9 (1), WHO definesmaternal death as: to aggravation (by pregnancy) of an existing cardiac or renal disease are considered the death of a woman while pregnant or indirect maternal deaths. within 42 days of termination of pregnancy, irrespective of the duration and site of A late maternal death is “the death of the pregnancy, from any cause related a woman from direct or indirect obstetric to or aggravated by the pregnancy or its causes, more than 42 days but less than one management but not from unintentional or year after termination of pregnancy” (1). Like incidental causes.10 maternal deaths, late maternal deaths also include both direct and indirect maternal/ This definition allows identification of a obstetric deaths. Complications of pregnancy maternal death, based on the cause of the or childbirth can lead to death beyond the death being identified as either a direct or six-week (42-day) postpartum period, and the indirect maternal cause. increased availability of modern life-sustaining procedures and technologies enables more Direct obstetric deaths (or direct women to survive adverse outcomes of maternal deaths) are those “resulting from pregnancy and delivery, and also delays obstetric complications of the pregnant state some deaths beyond that postpartum period. (pregnancy, labour and puerperium), and from Specific codes for “late maternal deaths” interventions, omissions, incorrect treatment, are included in the ICD-10 (O96 and O97) to or from a chain of events resulting from any capture these delayed maternal deaths, which of the above” (1). Deaths due to obstetric may not be categorized as maternal deaths haemorrhage or hypertensive disorders in in CRVS systems despite being caused by pregnancy, for example, or those due to pregnancy-related events (2). complications of anaesthesia or caesarean section are classified as direct maternal Maternal deaths and late maternal deaths deaths. are combined in the 11th revision of the ICD under the new grouping of “comprehensive Indirect obstetric deaths (or indirect maternal deaths” (1). maternal deaths) are those maternal deaths A death occurring during pregnancy, childbirth and puerperium (also known 9 ICD-11 (the 11th revision of the ICD) was adopted by the as a pregnancy-related death) is defined World Health Assembly in May 2019 and comes into effect on 1 January 2022. Further information is available at: as: “the death of a woman while pregnant or www.who.int/classifications/icd/en/. The coding rules within 42 days of termination of pregnancy, related to maternal mortality are being edited to fully match the new structure of ICD-11, but without changing the irrespective of the cause of death (obstetric resulting statistics. At the time of this writing, therefore, information about ICD codes relates to ICD-10 (the 10th and non-obstetric)” (1); this definition includes revision of the ICD) (2). The ICD-11 rules can be accessed in unintentional/accidental and incidental the reference guide of ICD-11, at https://icd.who.int. 10 Care has been taken to ensure that the definition of causes. This definition allows measurement of maternal death used for international comparison of deaths that occur during pregnancy, childbirth mortality statistics remains stable over time, but the word “unintentional” has been used in the ICD-11 definition (1) in and puerperium while acknowledging that place of the word “accidental” which was previously used, in ICD-10 (2). such measurements do not strictly conform
8 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 to the standard “maternal death” concept in and for the calculation of maternal mortality settings where accurate information about ratios and rates (i.e. excluding late maternal causes of death based on medical certification deaths).12,13 is unavailable. For instance, in maternal mortality surveys (such as those employing the The number of maternal deaths in a sisterhood method), relatives of a woman of population (during a specified time period, reproductive age who has died are asked about usually one calendar year) reflects two factors: her pregnancy status at the time of death (i) the risk of mortality associated with a single without eliciting any further information on the pregnancy or a single birth (whether live birth cause or circumstances of the death. These or stillbirth); and (ii) the fertility level (i.e. the surveys usually measure deaths to women number of pregnancies or births that are during pregnancy, childbirth and puerperium experienced by women of reproductive age, (pregnancy-related deaths) rather than i.e. age 15–49 years). maternal deaths. The maternal mortality ratio (MMR) is HIV-related indirect maternal deaths are defined as the number of maternal deaths deaths to HIV-positive women caused by the during a given time period per 100 000 live aggravating effect(s) of pregnancy on HIV; births during the same time period; thus, it where the interaction between pregnancy and quantifies the risk of maternal death relative HIV becomes the underlying cause of death, to the number of live births, and essentially these are counted as indirect maternal deaths. captures the first factor mentioned above. There is an ICD code – O98.7 (HIV disease complicating pregnancy, childbirth and the By contrast, the maternal mortality rate puerperium) – for identifying HIV-related (MMRate) is defined and calculated as the indirect maternal deaths.11 number of maternal deaths divided by person- years lived by women of reproductive age in a Incidental HIV deaths are deaths caused by population. The MMRate captures both the risk HIV/AIDS which occur to women who happen of maternal death per pregnancy or per birth to be pregnant, in labour or postpartum (also (whether live birth or stillbirth), and the level defined as “HIV-related deaths to women of fertility in the population (i.e. both factors during pregnancy, delivery or puerperium” [3]); mentioned above). these are not maternal deaths and would not be included in the calculation of MMR. In addition, it is possible to calculate the adult lifetime risk of maternal death for women in All the types and definitions of deaths the population, defined as the probability that described above (as used in this report) are a 15-year-old girl (in the year of the estimate) summarized in Table 2.1. will eventually die from a maternal cause. This indicator takes into account competing causes 2.2 Measures of maternal mortality used in this report 12 ICD-11, Part 2, section 2.28.5.7: “International reporting of maternal mortality: For the purpose of the international reporting of maternal mortality, only those maternal deaths As indicated in the ICD-11 (and previously in occurring before the end of the 42-day reference period the ICD-10), only maternal deaths occurring up should be included in the calculation of the various ratios and rates, although the recording of later deaths is useful for to 42 days postpartum are considered relevant national analytical purposes” (1). for the purposes of international reporting 13 Late maternal deaths coded to O96 (late maternal deaths) and O97 (late maternal deaths due to sequalae of complications) are also of interest for national- and 11 Search for O98.7 at the current (2016) version of ICD-10: international-level analysis, but are not reported in this https://icd.who.int/browse10/2016/en. publication.
9 Definitions and measures Table 2.1. Types and definitions of deaths occurring during pregnancy, childbirth and puerperium (also known as “pregnancy-related deaths”)
Maternal deaths Non-maternal deaths
Non-HIV- Non-HIV-related maternal deaths: Non-HIV-related, non- related deaths • Maternal death – the death of a woman while pregnant or within 42 days maternal deaths – deaths (the woman may to pregnant and postpartum or may not have of termination of pregnancy, irrespective of the duration and site of the women from unintentional/ had HIV) pregnancy, from any cause related to or aggravated by the pregnancy or accidental or incidental its management but not from unintentional or incidental causes causes other than HIV —— Direct obstetric/maternal deaths – deaths resulting from complications of pregnancy/delivery/postpartum (up to 42 days), from interventions, omissions or incorrect treatment, or from a chain of events resulting from any of the above —— Indirect obstetric/maternal deaths – deaths due to a disease (other than HIV) aggravated by the effects of pregnancy
• Late maternal deaths – direct or indirect maternal deaths occurring from 42 days to 1 year after termination of pregnancy
HIV-related HIV-related maternal deaths: HIV-related, non- deaths maternal deaths: • HIV-related indirect maternal deaths – deaths to HIV-positive women (the woman was caused by the aggravating effects of pregnancy on HIV • Incidental HIV deaths known to have – deaths caused by HIV/ had HIV) • HIV-related indirect late maternal deaths – deaths to HIV-positive AIDS which occur to women 42 days to 1 year after termination of pregnancy, caused by the women who happen to aggravating effects of pregnancy on HIV be pregnant, in labour or postpartum
of death (4). The formula for calculating this such that the model has to account for the measure is given in Chapter 3, section 3.3.3. difference in definitions (see Chapter 3, section 3.3.2: BMat model). An alternative measure of maternal mortality, the proportion maternal (PM), is the For further information on ICD coding and proportion of deaths among women of approaches to measuring maternal mortality, reproductive age that are due to maternal see Annex 2. causes; PM is calculated as the number of maternal deaths in a given time period divided by the total deaths among women aged 15–49 years in that time period. Although by definition PM refers strictly to maternal deaths (and the estimation model described in Chapter 3 is based on this definition), some observed (documented) PMs actually use a “pregnancy-related” definition (and not all pregnancy-related deaths are maternal deaths, as defined in section 2.1 above),
10 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Box A2.1. STATISTICAL MEASURES OF MATERNAL MORTALITY
Maternal mortality ratio (MMR): Number of maternal deaths during a given time period per 100 000 live births during the same time period (5).
Maternal mortality rate (MMRate): Number of maternal deaths during a given time period divided by person-years lived by women of reproductive age (age 15–49 years) in a population during the same time period (6).
Adult lifetime risk of maternal death: The probability that a 15-year-old woman will eventually die from a maternal cause (4).
The proportion of deaths among women of reproductive age that are due to maternal causes (proportion maternal; PM): The number of maternal deaths divided by the total deaths among women aged 15–49 years (5).
References
1. 2.28.5 Standards and reporting requirements 5. World Health Organization (WHO), United related for maternal mortality. In: ICD-11 Nations Children’s Fund (UNICEF), United Reference guide, Part 2. Geneva: World Nations Population Fund (UNFPA), World Bank Health Organization; 2019 (https://icd.who. Group, United Nations Population Division. int/icd11refguide/en/index.html#2.28.5Sta Trends in maternal mortality: 1990 to 2015: ndardsMarternalMortaltiy|standards-and- estimates by WHO, UNICEF, UNFPA, World reporting-requirements-related-for-maternal- Bank Group and the United Nations Population mortality|c2-28-5, accessed 12 July 2019). Division. Geneva: WHO; 2015 (https://www. who.int/reproductivehealth/publications/ 2. International statistical classification of monitoring/maternal-mortality-2015/en/, diseases and related health problems, 10th accessed 4 September 2019). revision. Volume 2: Instruction manual. Geneva; World Health Organization; 2010 6. Wilmoth J, Mizoguchi N, Oestergaard M, (https://www.who.int/classifications/icd/ Say L, Mathers C, Zureick-Brown S, et al. A ICD10Volume2_en_2010.pdf, accessed 10 new method for deriving global estimates June 2019). of maternal mortality. Stat Politics Policy. 2012;3(2):2151-7509.1038. 3. The WHO application of ICD-10 to deaths during pregnancy, childbirth and puerperium: ICD-MM. Geneva: World Health Organization; 2012 (https://www. who.int/reproductivehealth/publications/ monitoring/9789241548458/en/, accessed 4 September 2019).
4. Wilmoth J. The lifetime risk of maternal mortality: concept and measurement. Bull World Health Organ. 2009;87:256-62. doi:10.2471/BLT.07.048280.
11 Definitions and measures 03
12 © WHO PAHO CONTENT
14 Data inputs for the estimation process 17 Other data inputs to the model 18 Statistical methods TRENDS 03IN MATERNAL MORTALITY METHODS
Previously, in 2010, 2012, 2014 and 2015, the United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG) published reports on maternal mortality trends (including data up to 2008, 2010, 2013 and 2015, respectively) with advice from an external technical advisory group (TAG) (1–4). The methods described here for developing estimates of levels and trends of maternal mortality between 2000 and 2017 build upon the methods used in those previous rounds (5,6,7). The key change to the estimation methodology and resulting estimates in this round is described in section 3.3 (Statistical methods) and concerns the adjustment of data from countries’ civil registration and vital statistics (CRVS) systems (section 3.3.1). CRVS data have been adjusted in previous rounds to account for unregistered and/or misclassified maternal deaths (see definitions in Box 3.1). The UN MMEIG has considered concerns from Member States about how this adjustment was calculated, and how it may or may not have reflected improvements in data collection and data quality related to maternal mortality over time.
Combined with the updated global maternal mortality database,14 the UN MMEIG Bayesian
14 WHO Mortality Database: https://www.who.int/healthinfo/ mortality_data/en/ (select indicator for “pregnancy, childbirth and the puerperium”).
© WHO PAHO 13 Box 3.1. DEFINITIONS OF INCOMPLETENESS (UNREGISTERED) AND MISCLASSIFICATION OF MATERNAL DEATHS*
Incompleteness Incompleteness refers to unregistered deaths (also known as “missing”) – i.e. deaths not registered in the CRVS system – resulting in an incomplete CRVS system. This can arise due to both incomplete identification/registration of individual deaths in each country and incomplete coverage of the national CRVS system within each country.
We distinguish between non-maternal deaths not registered in the CRVS system (U–), and maternal deaths not registered in the CRVS system (U+) (see section 3.3.1.a).
Misclassification Misclassification refers to incorrect coding of deaths registered within the CRVS system, due either to error in the medical certification of cause of death or error in applying the ICD code.
We distinguish between maternal deaths incorrectly classified as non-maternal deaths (false negatives; F–), and non-maternal deaths incorrectly classified as maternal deaths (false positives, F+) (see section 3.3.1.a).
* Incompleteness and misclassification are often referred to collectively or individually as “underreporting”, but we suggest not to use this term and instead to be clear about exactly which issue is being referred to, whether incompleteness (unregistered), misclassification, or both.
maternal mortality estimation (BMat) model 3.1 Data inputs for the estimation (see section 3.3.2) provides the most up-to- process date maternal mortality estimates yet for the entire 2000–2017 timespan. These results 3.1.1 Data sources supersede all previously published estimates for years within that time period, and due Maternal mortality ratio (MMR) estimates to modifications in methodology and data are based on a variety of data sources – availability, differences between these and including data from CRVS systems, which previous estimates should not be interpreted are the preferred data source (considered as representing time trends. The full database, to be the gold standard for mortality data), country profiles and all model specification population-based household surveys using the codes used are available online.15 sisterhood method, reproductive-age mortality studies (RAMOS), confidential enquires into maternal deaths (CEMD), verbal autopsies, censuses and other specialized maternal mortality studies conducted at the national level. What is needed for the country-level 15 Available at: www.who.int/reproductivehealth/ estimates is a robust, accurate, nationally publications/maternal-mortality-2017/en/.
14 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 representative data source, for which there is • If the ratio is less than 0.95 for one or more clear information about the data collection and years, the completeness is given by the checking methods; this data source may or ratio for each individual year. may not be the national CRVS system. The UN • After obtaining an estimate of MMEIG global maternal mortality estimation completeness, we combine this estimate input database has been updated since the with the proportion of deaths that have last round of estimates in 2015. The new been assigned to an ill defined code. draft estimates were shared with countries We exclude observations for which the during the 2019 country consultation period estimated percentage of deaths that May–June 2019 (see Annex 1), after which are assigned to a well defined code the estimates and the database were updated is lower than 60%. In other words, if again in July 2019 prior to the final run of the completeness proportion*(1 – proportion UN MMEIG BMat model. ill defined)*100% > 60%, the observation is a. Civil registration and vital statistics (CRVS) included (4).
b. Specialized studies on maternal mortality For countries that routinely register deaths and apply the medical certificate of cause of death Over recent decades, efforts have been (MCCD), maternal deaths may be incorrectly undertaken in certain settings to measure reported due to unregistered deaths and/or maternal mortality using CRVS data in deaths that are misclassified in terms of ICD combination with further data collection on coding. To account for potential unregistered maternal deaths, sometimes also enhancing deaths as well as misclassification in CRVS the quality of the CRVS systems. In some data, an adjustment is calculated for each cases, a specialized study is conducted CRVS input data point (see section 3.3.1) for the purpose of assessing the extent of before it is included in the BMat model (see misclassification within the CRVS system (i.e. section 3.3.2). independent assessment of cause of death classification among the deaths that were For each country with CRVS data, the level registered as maternal deaths – to check if of completeness of the CRVS, in terms they are “true positives” – and among other of registration of all deaths to females of registered deaths to women of reproductive reproductive age (i.e. fewer unregistered age that were not registered as maternal deaths means the CRVS data are more deaths but which might have been “false complete), is estimated as follows. negatives”). CEMD is an example of a method used for these types of studies. In other cases, • We calculate the annual ratio of female a specialized study is conducted to assess the deaths reported in the CRVS system extent of “missingness” of maternal deaths in divided by female deaths estimated by the CRVS system, by using other methods to WHO for all years with CRVS data, based on document additional unregistered maternal a moving window of five-year periods (five- deaths that have occurred in a specified year periods are used to obtain smoothed geographic area (e.g. RAMOS). estimates of completeness) (8).
• If the ratio (in particular, the upper bound These data sources typically expand the of the 80% uncertainty interval on the ratio) scope of their reviews to the entire number is greater than 0.95 for all years with CRVS of deaths among women of reproductive age data, we assume that the CRVS is complete (15–49 years) in a country and triangulate in the country. information from sources including, but not
15 Methods limited to: medical/hospital records, police MMR was available from the data source, records, surveillance systems, national the observed MMR was converted into a PM, registries, death certificates, census, medical again using estimates of all-cause deaths autopsy, and administrative reviews between among females aged 15–49 and live births. national statistical offices and ministries of An upward adjustment of 10% was applied to health. The information reported by these all observations that were not obtained from specialized studies varies greatly, and includes CRVS or specialized studies, to account for any combination of the following: total deaths early in pregnancy that might not have number of deaths to women of reproductive been captured (4). age and/or total number of maternal deaths; all causes of death correctly documented The available data sources provide calculated among all women of reproductive age and/ PMs according to two definitions: “maternal” or or all causes of maternal deaths; unregistered “pregnancy-related” deaths (see Chapter 2). deaths to women of reproductive age and/ PMs for pregnancy-related deaths excluding or unregistered maternal deaths. In these accidents were taken as measures of maternal situations, it is agreed that no adjustment PM without further adjustment. Based on an factor needs to be applied, and so analysis of measured levels of maternal versus observations from specialized studies are pregnancy-related death from sources where included in the BMat model (see section 3.3.2) both quantities were reported, and of injury without adjustment. death rates among women of reproductive age using WHO estimates of cause-specific c. Other data sources for maternal mortality mortality for Member States, the UN MMEIG/ TAG agreed to estimate “maternal” deaths Other available data sources include data from from the PM for “pregnancy-related” deaths, surveillance sites or systems, population- based on assumptions that incidental or based surveys and censuses. From these data accidental deaths (i.e. not maternal deaths) sources, for the purposes of estimation, the comprise 10% of pregnancy-related deaths observed proportion of maternal deaths (PM) (excluding HIV-related deaths) in sub-Saharan among all deaths to women aged 15–49 years African countries, and 15% in other low- and was taken as the preferred indicator for use in middle-income countries (1). estimating maternal mortality.
Table 3.1 gives an overview of data used The PM is preferred over observed MMRs or to produce maternal mortality estimates. other summary outcomes because it is less Further information about sources of maternal affected by unregistered deaths: deaths to mortality data is provided in Annex 2. women aged 15–49 that are unregistered would potentially affect the numerator and 3.1.2 Uncertainty associated with the denominator of the PM proportionately observations and adjustments if causes of death are not unregistered differentially. Therefore, in processing data All observed death counts and PMs are subject related to maternal mortality, observed PMs to random error, in the form of sampling error took priority over observed MMRs, and for (for PMs obtained from surveys), stochastic each observed PM, the corresponding MMR error (for PMs obtained from a small number is calculated based on the United Nations of deaths) and/or non-sampling error (i.e. Population Division (UNPD) estimates of random errors that may occur at any point live births (9) and all-cause deaths among during the data-collection process). females aged 15–49 (WHO estimates) (8) for the respective country-period. If only the
16 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Table 3.1. Maternal mortality data records by source type used in generating the 2000–2017 estimates for maternal mortality
Number of Number of Source type records country-years
Civil registration and vital statistics (CRVS) 2204 2204
Specialized studies on maternal mortality 376 534
Other sources – reporting on maternal mortality 188 216
Other sources – reporting on pregnancy-related mortality 207 116 9
All 2975 4123 a
a The sum of country-years of data has been rounded.
To account for the uncertainty associated a subset of all deaths was accounted for with with these errors, and thus the uncertainty regard to data from incomplete CRVS systems, associated with the PM, error variances were and specialized studies with study populations calculated. For observations from CRVS that were limited to a subset of all-cause or confidential enquiries, stochastic error deaths. variances were obtained, which quantify the uncertainty associated with the true risk of a The WHO life tables (8) include “mortality maternal death, based on the available data. shocks”. Annex 3 describes how these are For observed PMs from surveys and other dealt with in the context of maternal mortality. maternal mortality studies, the error variance was a combination of the sampling variance 3.2. Other data inputs to the model associated with the survey and an additional non-sampling error. The non-sampling error 3.2.1 Data on all deaths to women aged was estimated based on the UN MMEIG 15–49 years and HIV-related mortality maternal mortality database (5). For all observed PMs, the error variances were taken We used a set of consistent external estimates into account when obtaining PM and thus for deaths due to HIV from the Joint United MMR estimates: observations with smaller Nations Programme on HIV/AIDS (UNAIDS) error variances are more informative of the (10) and estimates for deaths among females true PM and will thus carry a greater weight aged 15–49 years from WHO life tables (8). in determining the estimates compared with These agencies revise their estimates on a observations with larger error variances. regular basis to take into account new data and Additionally, uncertainty associated with improved methods. Any comments regarding adjustments (e.g. the CRVS adjustment as per these input indicators should be addressed to the new approach described in section 3.3.1, the respective agencies.16 and adjustment of observations which report “pregnancy-related” deaths) was accounted for. Lastly, uncertainty due to capturing only 16 For UNAIDS mortality estimates: [email protected]; for WHO life tables: [email protected].
17 Methods 3.2.2 Live births data of live births with a skilled birth attendant (SBA) at the time of delivery serves as a direct For the preliminary MMR estimates shared measure of the conditions under which births during the 2019 country consultations, inputs occur in a given population (6). for live births were taken from the UNPD’s 2019 revision of World population prospects (9). In Time series of annual estimates for the this publication, the UNPD produced estimates following three predictor variables (covariates) of population and related indicators (e.g. births were constructed from 1990 to 2017. and deaths) for countries or areas, covering five-year periods from 1950–1955 through to • Gross domestic product (GDP) per capita, 2010–2015, as well as projections covering measured in purchasing power parity (PPP) five-year periods from 2015–2020 through to equivalent US dollars using 2011 as the 2095–2100. For countries with well functioning baseline, was generated based on data CRVS systems, UNPD used data on births by from the World Bank Group (11). age of the mother together with population • General fertility rate (GFR) was computed data by age and sex from censuses and official from data on live births and population statistics to estimate age-specific fertility size (number of women aged 15–49) from rates (ASFR) for each historical and future UNPD’s 2019 revision of World population five-year period. The population estimation prospects (9). and projection procedure used the ASFR and other inputs such as age- and sex-specific • Skilled birth attendant (SBA) data consist of mortality rates to generate a consistent time time series derived using all available data series of population size, age distribution, and from population-based national household the demographic components of population survey data and countries’ routine change (births, deaths and migration). reporting mechanisms (WHO and UNICEF Annual estimates of births are obtained by Joint Skilled Birth Attendant database [12]). interpolating the five-year estimates of the For further details related to the predictor number of births output, using the population variables, please refer to Annex 4. estimation and projection procedure. As a result, the annually interpolated national 3.3. Statistical methods estimates do not necessarily match the annual numbers of births reported in the individual We use two models, for different purposes. countries’ CRVS systems.17
1. The CRVS model: For countries that have 3.2.3 Predictor variables in the maternal a CRVS system, we use a Bayesian CRVS mortality model adjustment model to account for errors in reporting of maternal death in the CRVS to The predictor variables used in the BMat obtain the CRVS adjustment factors. model fall into three categories: indicators of socioeconomic development, measures 2. The BMat model: For all countries, we of fertility and process variables. In the final use a Bayesian maternal mortality estimation model, the gross domestic product per model to estimate the MMR for each country- capita (GDP) represents socioeconomic year of interest. development, fertility is measured by the general fertility rate (GFR), and the proportion To estimate MMR for country-years, we first use the CRVS model to obtain the CRVS
17 Any comments regarding the estimates of live births from adjustment factors. These adjustment factors UNPD should be addressed to: [email protected].
18 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 are then applied in the BMat model to estimate This section explains: the MMR for each country-year of interest (see a. Types of reporting errors encountered in Figure 3.1). The CRVS model is described in CRVS systems section 3.3.1, followed by the description of the BMat model in section 3.3.2. b. Summary metrics for reporting errors
c. Deriving sensitivity, specificity and CRVS 3.3.1 Bayesian CRVS adjustment model adjustments from the CRVS model to account for errors in reporting of maternal death in the CRVS system d. Comparison with previous UN MMEIG (the CRVS model) approach to estimate CRVS adjustment factors. Relying on maternal deaths as reported in the The model used to estimate the CRVS data- CRVS system means there is a potential for quality parameters, and corresponding error due to unregistered maternal deaths and/ adjustment factors for CRVS data in BMat are or misclassification of the cause of death within summarized here below (subsections a–d) and the CRVS system. Therefore, an adjustment described in detail in a separate publication by factor is obtained for CRVS data before it is Peterson et al. (13). included in the BMat model (section 3.3.2).
Figure 3.1. Overview of modelling steps for MMR estimation
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